Paper Info
Title
Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup
Abstract
Recently released research about deep learning applications related to perception for autonomous driving focuses heavily on the usage of LiDAR point cloud data as input for the neural networks, highlighting the importance of LiDAR technology in the field of Autonomous Driving (AD). In this sense, a great percentage of the vehicle platforms used to create the datasets released for the development of these neural networks, as well as some AD commercial solutions available on the market, heavily invest in an array of sensors, including a large number of sensors as well as several sensor modalities. However, these costs create a barrier to entry for low-cost solutions for the performance of critical perception tasks such as Object Detection and SLAM. This paper explores current vehicle platforms and proposes a low-cost, LiDAR-based test vehicle platform capable of running critical perception tasks (Object Detection and SLAM) in real time. Additionally, we propose the creation of a deep learning-based inference model for Object Detection deployed in a resource-constrained device, as well as a graph-based SLAM implementation, providing important considerations, explored while taking into account the real-time processing requirement and presenting relevant results demonstrating the usability of the developed work in the context of the proposed low-cost platform.
Year
DOI
Venue
2021
10.3390/s21248381
SENSORS
Keywords
DocType
Volume
autonomous driving, deep learning methods, LiDAR scanners, 3D object detection, onboard inference, SLAM, vehicles setup
Journal
21
Issue
ISSN
Citations 
24
1424-8220
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Duarte Fernandes101.35
Tiago Afonso200.68
Pedro Girão300.68
Dibet Gonzalez400.34
António Silva573.69
Rafael Névoa601.01
Paulo Novais700.34
João Monteiro800.68
Pedro Melo-Pinto901.01